def test_summary2_exp(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(10, 1, "x.hex", 1, 20000, ("C1", None, None, None, None, None)),
(100, 1, "x.hex", 1, 20000, ("C1", None, None, None, None, None)),
(1000, 1, "x.hex", -5000, 0, ("C1", None, None, None, None, None)),
(10000, 1, "x.hex", -100000, 100000, ("C1", None, None, None, None, None)),
(100000, 1, "x.hex", -1, 1, ("C1", None, None, None, None, None)),
(1000000, 1, "A.hex", 1, 100, ("C1", None, None, None, None, None)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
# rangeMin and rangeMax are not used right now
for (rowCount, colCount, hex_key, rangeMin, rangeMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = "syn_" + "binary" + "_" + str(rowCount) + "x" + str(colCount) + ".csv"
csvPathname = SYNDATASETS_DIR + "/" + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(
csvPathname, rowCount, colCount, lambd=LAMBD, SEED=SEEDPERFILE
)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxDelta = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(
path=csvPathname, schema="put", hex_key=hex_key, timeoutSecs=30, doSummary=False
)
print "Parse result['destination_key']:", parseResult["destination_key"]
inspect = h2o_cmd.runInspect(None, parseResult["destination_key"])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("Summary2 summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult["summaries"][0]
colname = column["colname"]
coltype = column["type"]
nacnt = column["nacnt"]
stats = column["stats"]
stattype = stats["type"]
# FIX! we should compare mean and sd to expected?
mean = stats["mean"]
sd = stats["sd"]
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats["zeros"]
mins = stats["mins"]
maxs = stats["maxs"]
pct = stats["pct"]
expectedPct = [0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99]
pctile = stats["pctile"]
# the thresholds h2o used, should match what we expected
if expected[0]:
self.assertEqual(colname, expected[0])
if expected[1]:
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg="min is not approx. expected")
if expected[2]:
h2o_util.assertApproxEqual(
pctile[3], expected[2], tol=maxDelta, msg="25th percentile is not approx. expected"
)
if expected[3]:
h2o_util.assertApproxEqual(
pctile[5], expected[3], tol=maxDelta, msg="50th percentile (median) is not approx. expected"
)
if expected[4]:
h2o_util.assertApproxEqual(
pctile[7], expected[4], tol=maxDelta, msg="75th percentile is not approx. expected"
)
if expected[5]:
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg="max is not approx. expected")
hstart = column["hstart"]
hstep = column["hstep"]
hbrk = column["hbrk"]
hcnt = column["hcnt"]
print "pct:", pct
print ""
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
print "Can't estimate the bin distribution"
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname, "(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
if colname != "" and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype="float",
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
def test_quant_cmp_uniform(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(5*ROWS, 1, 'x.hex', 1, 20000, ['C1', 1.10, 5000.0, 10000.0, 15000.0, 20000.00]),
(5*ROWS, 1, 'x.hex', -5000, 0, ['C1', -5001.00, -3750.0, -2445, -1200.0, 99]),
(1*ROWS, 1, 'x.hex', -100000, 100000, ['C1', -100001.0, -50000.0, 1613.0, 50000.0, 100000.0]),
(1*ROWS, 1, 'x.hex', -1, 1, ['C1', -1.05, -0.48, 0.0087, 0.50, 1.00]),
(1*ROWS, 1, 'A.hex', 1, 100, ['C1', 1.05, 26.00, 51.00, 76.00, 100.0]),
(1*ROWS, 1, 'A.hex', -99, 99, ['C1', -99, -50.0, 0, 50.00, 99]),
(1*ROWS, 1, 'B.hex', 1, 10000, ['C1', 1.05, 2501.00, 5001.00, 7501.00, 10000.00]),
(1*ROWS, 1, 'B.hex', -100, 100, ['C1', -100.10, -50.0, 0.85, 51.7, 100,00]),
(1*ROWS, 1, 'C.hex', 1, 100000, ['C1', 1.05, 25002.00, 50002.00, 75002.00, 100000.00]),
(1*ROWS, 1, 'C.hex', -101, 101, ['C1', -100.10, -50.45, -1.18, 49.28, 100.00]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
# max error = half the bin size?
colname = expected[0]
maxDelta = ((expectedMax - expectedMin)/1000.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
# need the full pathname when python parses the csv for numpy/sort
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
#***************************
# Parse
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=30, doSummary=False)
pA = h2o_cmd.ParseObj(parseResult, expectedNumRows=rowCount, expectedNumCols=colCount)
numRows = pA.numRows
numCols = pA.numCols
parse_key = pA.parse_key
# this guy can take json object as first thing, or re-read with key
iA = h2o_cmd.InspectObj(parse_key,
expectedNumRows=rowCount, expectedNumCols=colCount, expectedMissinglist=[])
#***************************
# Summary
co = h2o_cmd.runSummary(key=parse_key)
default_pctiles = co.default_pctiles
coList = [ co.base, len(co.bins), len(co.data), co.domain,
co.label, co.maxs, co.mean, co.mins, co.missing, co.ninfs, co.pctiles,
co.pinfs, co.precision, co.sigma, co.str_data, co.stride, co.type, co.zeros]
for c in coList:
print c
print "len(co.bins):", len(co.bins)
print "co.label:", co.label, "mean (2 places):", h2o_util.twoDecimals(co.mean)
print "co.label:", co.label, "std dev. (2 places):", h2o_util.twoDecimals(co.sigma)
print "FIX! hacking the co.pctiles because it's short by two"
summ_pctiles = [0] + co.pctiles + [0]
pt = h2o_util.twoDecimals(summ_pctiles)
mx = h2o_util.twoDecimals(co.maxs)
mn = h2o_util.twoDecimals(co.mins)
exp = h2o_util.twoDecimals(expected[1:])
print "co.label:", co.label, "co.pctiles (2 places):", pt
print "default_pctiles:", default_pctiles
print "co.label:", co.label, "co.maxs: (2 places):", mx
print "co.label:", co.label, "co.mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
h2p.green_print("min/25/50/75/max co.label:", co.label, "(2 places):",\
mn[0], pt[3], pt[5], pt[7], mx[0])
h2p.green_print("min/25/50/75/max co.label:", co.label, "(2 places):",\
exp[0], exp[1], exp[2], exp[3], exp[4])
#***************************
# Quantile
# the thresholds h2o used, should match what we expected
# using + here seems to result in an odd tuple..doesn't look right to h2o param
# so went with this. Could add '[' and ']' to the list first, before the join.
probsStr = "[%s]" % ",".join(map(str,probsList))
parameters = {
'model_id': "a.hex",
'training_frame': parse_key,
'validation_frame': parse_key,
'ignored_columns': None,
'probs': probsStr,
}
model_key = 'qhex'
bmResult = h2o.n0.build_model(
algo='quantile',
model_id=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=10)
bm = OutputObj(bmResult, 'bm')
msec = bm.jobs[0]['msec']
print "bm msec", msec
# quantile result is just a job result to a key
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0], 'model')
print "model.output:", model.output
print "model.output:['quantiles']", model.output['quantiles']
print "model.output:['iterations']", model.output['iterations']
print "model.output:['names']", model.output['names']
quantiles = model.output['quantiles'][0] # why is this a double array
iterations = model.output['iterations']
assert iterations == 11, iterations
print "quantiles: ", quantiles
print "iterations: ", iterations
# cmmResult = h2o.n0.compute_model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
# cmm = OutputObj(cmmResult, 'cmm')
# mmResult = h2o.n0.model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
# mm = OutputObj(mmResult, 'mm')
# prResult = h2o.n0.predict(model=model_key, frame=parse_key, timeoutSecs=60)
# pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
trial += 1
# compare the last threshold
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=CHECK_PCTILE,
# h2oSummary2=pctile[-1],
# h2oQuantilesApprox=result, # from exec
h2oExecQuantiles=quantiles[CHECK_PCTILE_INDEX],
)
h2o.nodes[0].remove_all_keys()
def test_summary2_uniform(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, 1, 'x.hex', 1, 20000, ('C1', 1.10, 5000.0, 10000.0, 15000.0, 20000.00)),
(ROWS, 1, 'x.hex', -5000, 0, ('C1', -5001.00, -3750.0, -2445, -1200.0, 99)),
(ROWS, 1, 'x.hex', -100000, 100000, ('C1', -100001.0, -50000.0, 1613.0, 50000.0, 100000.0)),
(ROWS, 1, 'x.hex', -1, 1, ('C1', -1.05, -0.48, 0.0087, 0.50, 1.00)),
(ROWS, 1, 'A.hex', 1, 100, ('C1', 1.05, 26.00, 51.00, 76.00, 100.0)),
(ROWS, 1, 'A.hex', -99, 99, ('C1', -99, -50.0, 0, 50.00, 99)),
(ROWS, 1, 'B.hex', 1, 10000, ('C1', 1.05, 2501.00, 5001.00, 7501.00, 10000.00)),
(ROWS, 1, 'B.hex', -100, 100, ('C1', -100.10, -50.0, 0.85, 51.7, 100,00)),
(ROWS, 1, 'C.hex', 1, 100000, ('C1', 1.05, 25002.00, 50002.00, 75002.00, 100000.00)),
(ROWS, 1, 'C.hex', -101, 101, ('C1', -100.10, -50.45, -1.18, 49.28, 100.00)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
# max error = half the bin size?
maxDelta = ((expectedMax - expectedMin)/20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
h2o.beta_features = False
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
h2o.beta_features = False
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=30, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["num_rows"]
numCols = inspect["num_cols"]
h2o.beta_features = True
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
quantile = 0.5 if DO_MEDIAN else .999
# get both answers since we feed both below for checking
q = h2o.nodes[0].quantiles(source_key=hex_key, column=column['colname'],
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=2, interpolation_type=7) # linear
qresult = q['result']
qresult_single = q['result_single']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", q['iterations'])
h2p.blue_print("h2o quantiles interpolated:", q['interpolated'])
print h2o.dump_json(q)
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats['zeros']
mins = stats['mins']
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg='min is not approx. expected')
maxs = stats['maxs']
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg='max is not approx. expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct= [0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99]
pctile = stats['pctile']
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxDelta, msg='25th percentile is not approx. expected')
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxDelta, msg='50th percentile (median) is not approx. expected')
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxDelta, msg='75th percentile is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
# too hard to estimate when there are ints now, due to floor/ceil int alignment?
# don't check the last two bins
for b in hcnt[1:(-2 if len(hcnt)>2 else -1)]:
# should we be able to check for a uniform distribution in the files?
e = numRows/len(hcnt)
self.assertAlmostEqual(b, rowCount/len(hcnt), delta=.01*rowCount,
msg="Bins not right. b: %s e: %s" % (b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname, "(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
)
h2o.nodes[0].remove_all_keys()
def test_summary2_percentile2(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(500000, 2, 'cD', 300, 0, 9), # expectedMin/Max must cause 10 values
(500000, 2, 'cE', 300, 1, 10), # expectedMin/Max must cause 10 values
(500000, 2, 'cF', 300, 2, 11), # expectedMin/Max must cause 10 values
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
for (rowCount, colCount, hex_key, timeoutSecs, expectedMin, expectedMax) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
legalValues = {}
for x in range(expectedMin, expectedMax):
legalValues[x] = x
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
h2o.beta_features = False
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=30, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
summaryResult = h2o_cmd.runSummary(key=hex_key, cols=0, max_ncols=1)
if h2o.verbose:
print "summaryResult:", h2o.dump_json(summaryResult)
summaries = summaryResult['summaries']
scipyCol = 0
for column in summaries:
colname = column['colname']
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
pctile = stats['pctile']
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hcnt:
e = .1 * rowCount
self.assertAlmostEqual(b, .1 * rowCount, delta=.01*rowCount,
msg="Bins not right. b: %s e: %s" % (b, e))
print "pctile:", pctile
print "maxs:", maxs
self.assertEqual(maxs[0], expectedMax)
print "mins:", mins
self.assertEqual(mins[0], expectedMin)
for v in pctile:
self.assertTrue(v >= expectedMin,
"Percentile value %s should all be >= the min dataset value %s" % (v, expectedMin))
self.assertTrue(v <= expectedMax,
"Percentile value %s should all be <= the max dataset value %s" % (v, expectedMax))
eV1 = [1.0, 1.0, 1.0, 3.0, 4.0, 5.0, 7.0, 8.0, 9.0, 10.0, 10.0]
if expectedMin==1:
eV = eV1
elif expectedMin==0:
eV = [e-1 for e in eV1]
elif expectedMin==2:
eV = [e+1 for e in eV1]
else:
raise Exception("Test doesn't have the expected percentileValues for expectedMin: %s" % expectedMin)
trial += 1
# if colname!='' and expected[scipyCol]:
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
scipyCol += 1
def test_summary2_exp(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# co.label, (min, 25th, 50th, 75th, max)
# parse setup error ? supposedly fixed now
# (1, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(10, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(100, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(1000, 1, 'x.hex', -5000, 0, ['C1', None, None, None, None, None]),
(10000, 1, 'x.hex', -100000, 100000,
['C1', None, None, None, None, None]),
(100000, 1, 'x.hex', -1, 1, ['C1', None, None, None, None, None]),
(1000000, 1, 'A.hex', 1, 100, ['C1', None, None, None, None,
None]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, rangeMin, rangeMax,
expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(csvPathname,
rowCount,
colCount,
lambd=LAMBD,
SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxErr = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxErr = 1.05 * maxErr
expected[1] = expectedMin
expected[5] = expectedMax
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
print pA.numRows, pA.numCols, pA.parse_key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
# column 0 not used here
assert len(expected) == 6
co = h2o_cmd.runSummary(key=hex_key,
column=0,
expected=expected[1:],
maxDelta=maxErr)
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
print "maxErr", maxErr
if co.label != '' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=False,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.99,
h2oSummary2=co.percentiles[5 if DO_MEDIAN else 9],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
def test_summary2_unifiles(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
# new with 1000 bins. copy expected from R
tryList = [
('cars.csv', 'c.hex', [
(None, None,None,None,None,None),
('economy (mpg)', None,None,None,None,None),
('cylinders', None,None,None,None,None),
],
),
('runifA.csv', 'A.hex', [
(None, 1.00, 25.00, 50.00, 75.00, 100.0),
('x', -99.9, -44.7, 8.26, 58.00, 91.7),
],
),
# colname, (min, 25th, 50th, 75th, max)
('runif.csv', 'x.hex', [
(None, 1.00, 5000.0, 10000.0, 15000.0, 20000.00),
('D', -5000.00, -3735.0, -2443, -1187.0, 99.8),
('E', -100000.0, -49208.0, 1783.8, 50621.9, 100000.0),
('F', -1.00, -0.4886, 0.00868, 0.5048, 1.00),
],
),
('runifB.csv', 'B.hex', [
(None, 1.00, 2501.00, 5001.00, 7501.00, 10000.00),
('x', -100.00, -50.1, 0.974, 51.7, 100,00),
],
),
('runifC.csv', 'C.hex', [
(None, 1.00, 25002.00, 50002.00, 75002.00, 100000.00),
('x', -100.00, -50.45, -1.135, 49.28, 100.00),
],
),
]
timeoutSecs = 15
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
timeoutSecs = 60
for (csvFilename, hex_key, expectedCols) in tryList:
csvPathname = csvFilename
csvPathnameFull = h2i.find_folder_and_filename('smalldata', csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(bucket='smalldata', path=csvPathname,
schema='put', hex_key=hex_key, timeoutSecs=10, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
# okay to get more cols than we want
# okay to vary MAX_QBINS because we adjust the expected accuracy
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
summaries = summaryResult['summaries']
scipyCol = 0
for expected, column in zip(expectedCols, summaries):
colname = column['colname']
if expected[0]:
self.assertEqual(colname, expected[0]), colname, expected[0]
else:
# if the colname is None, skip it (so we don't barf on strings on the h2o quantile page
scipyCol += 1
continue
quantile = 0.5 if DO_MEDIAN else .999
# h2o has problem if a list of columns (or dictionary) is passed to 'column' param
q = h2o.nodes[0].quantiles(source_key=hex_key, column=column['colname'],
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=2, interpolation_type=7) # for comparing to summary2
qresult = q['result']
qresult_single = q['result_single']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", q['iterations'])
h2p.blue_print("h2o quantiles interpolated:", q['interpolated'])
print h2o.dump_json(q)
# ('', '1.00', '25002.00', '50002.00', '75002.00', '100000.00'),
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
print stattype
# FIX! we should compare mean and sd to expected?
# enums don't have mean or sd?
if stattype!='Enum':
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
pct = stats['pct']
print "pct:", pct
print ""
# the thresholds h2o used, should match what we expected
expectedPct= [0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99]
pctile = stats['pctile']
# figure out the expected max error
# use this for comparing to sklearn/sort
if expected[1] and expected[5]:
expectedRange = expected[5] - expected[1]
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange/(MAX_QBINS-2)
maxErr = 0.5 * expectedBin # should we have some fuzz for fp?
else:
print "Test won't calculate max expected error"
maxErr = 0
# hack..assume just one None is enough to ignore for cars.csv
if expected[1]:
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxErr, msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxErr, msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxErr, msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxErr, msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxErr, msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hcnt:
# should we be able to check for a uniform distribution in the files?
e = .1 * numRows
# self.assertAlmostEqual(b, .1 * rowCount, delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
if stattype!='Enum':
pt = h2o_util.twoDecimals(pctile)
print "colname:", colname, "pctile (2 places):", pt
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
actual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", actual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
# need to ignore the car names
if colname!='' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
# FIX! ignore for now
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
if False and h2o_util.approxEqual(pctile[5], 0.990238116744, tol=0.002, msg='stop here'):
raise Exception("stopping to look")
scipyCol += 1
trial += 1
def test_summary2_uniform_int_w_NA(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
M = 100
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, 1, "B.hex", 1, 1000 * M, ("C1", 1.0 * M, 250.0 * M, 500.0 * M, 750.0 * M, 1000.0 * M)),
(ROWS, 1, "B.hex", 1, 1000, ("C1", 1.0, 250.0, 500.0, 750.0, 1000.0)),
(ROWS, 1, "x.hex", 1, 20000, ("C1", 1.0, 5000.0, 10000.0, 15000.0, 20000.0)),
(ROWS, 1, "x.hex", -5000, 0, ("C1", -5000.00, -3750.0, -2500.0, -1250.0, 0)),
(ROWS, 1, "x.hex", -100000, 100000, ("C1", -100000.0, -50000.0, 0, 50000.0, 100000.0)),
# (ROWS, 1, 'A.hex', 1, 101, ('C1', 1.0, 26.00, 51.00, 76.00, 101.0)),
# (ROWS, 1, 'A.hex', -99, 99, ('C1', -99, -49.0, 0, 49.00, 99)),
(ROWS, 1, "B.hex", 1, 10000, ("C1", 1.0, 2501.0, 5001.0, 7501.0, 10000.0)),
(ROWS, 1, "B.hex", -100, 100, ("C1", -100.0, -50.0, 0.0, 50.0, 100.0)),
(ROWS, 1, "C.hex", 1, 100000, ("C1", 1.0, 25001.0, 50001.0, 75001.0, 100000.0)),
# (ROWS, 1, 'C.hex', -101, 101, ('C1', -101, -51, -1, 49.0, 100.0)),
]
if not DO_REAL:
# only 3 integer values!
tryList.append((1000000, 1, "x.hex", -1, 1, ("C1", -1.0, -1, 0.000, 1, 1.00)))
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
# max error = half the bin size?
maxDelta = (expectedMax - expectedMin) / (MAX_QBINS + 0.0)
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
# also need to add some variance due to random distribution?
# maybe a percentage of the mean
distMean = (expectedMax - expectedMin) / 2
maxShift = distMean * 0.01
maxDelta = maxDelta + maxShift
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = "syn_" + "binary" + "_" + str(rowCount) + "x" + str(colCount) + ".csv"
csvPathname = SYNDATASETS_DIR + "/" + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(
path=csvPathname, schema="put", hex_key=hex_key, timeoutSecs=60, doSummary=False
)
print "Parse result['destination_key']:", parseResult["destination_key"]
inspect = h2o_cmd.runInspect(None, parseResult["destination_key"])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult["summaries"][0]
colname = column["colname"]
self.assertEqual(colname, expected[0])
coltype = column["type"]
nacnt = column["nacnt"]
stats = column["stats"]
stattype = stats["type"]
# FIX! we should compare mean and sd to expected?
mean = stats["mean"]
sd = stats["sd"]
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats["zeros"]
mins = stats["mins"]
maxs = stats["maxs"]
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg="min is not approx. expected")
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg="max is not approx. expected")
pct = stats["pct"]
# the thresholds h2o used, should match what we expected
expectedPct = [0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99]
pctile = stats["pctile"]
h2o_util.assertApproxEqual(
pctile[3], expected[2], tol=maxDelta, msg="25th percentile is not approx. expected"
)
h2o_util.assertApproxEqual(
pctile[5], expected[3], tol=maxDelta, msg="50th percentile (median) is not approx. expected"
)
h2o_util.assertApproxEqual(
pctile[7], expected[4], tol=maxDelta, msg="75th percentile is not approx. expected"
)
hstart = column["hstart"]
hstep = column["hstep"]
hbrk = column["hbrk"]
hcnt = column["hcnt"]
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(hcnt) # expect 21 thresholds, so 20 bins. each 5% of rows (uniform distribution)
# don't check the edge bins
self.assertAlmostEqual(
b, rowCount / len(hcnt), delta=0.01 * rowCount, msg="Bins not right. b: %s e: %s" % (b, e)
)
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname, "(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
scipyCol = 0
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname != "":
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype="float",
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
h2o.nodes[0].remove_all_keys()
def test_summary2_exp(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(10, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(100, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(1000, 1, 'x.hex', -5000, 0, ['C1', None, None, None, None, None]),
(10000, 1, 'x.hex', -100000, 100000, ['C1', None, None, None, None, None]),
(100000, 1, 'x.hex', -1, 1, ['C1', None, None, None, None, None]),
(1000000, 1, 'A.hex', 1, 100, ['C1', None, None, None, None, None]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
# rangeMin and rangeMax are not used right now
for (rowCount, colCount, hex_key, rangeMin, rangeMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(csvPathname,
rowCount, colCount, lambd=LAMBD, SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxDelta = ((expectedMax - expectedMin)/20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
expected[1] = expectedMin
expected[5] = expectedMax
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', header=0, hex_key=hex_key, timeoutSecs=30, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("Summary2 summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
expectedPct= [0.001, 0.001, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.99, 0.999]
pctile = stats['pctile']
# the thresholds h2o used, should match what we expected
if expected[0]:
self.assertEqual(colname, expected[0])
if expected[1]:
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxDelta, msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxDelta, msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxDelta, msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print ""
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
print "Can't estimate the bin distribution"
# figure out the expected max error
# use this for comparing to sklearn/sort
if expected[1] and expected[5]:
expectedRange = expected[5] - expected[1]
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange/(MAX_QBINS-2)
maxErr = expectedBin # should we have some fuzz for fp?
else:
print "Test won't calculate max expected error"
maxErr = 0
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname, "(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
if colname!='' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=False,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
def test_summary2_percentile2(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(500000, 2, 'cD', 300, 0, 9), # expectedMin/Max must cause 10 values
(500000, 2, 'cE', 300, 1, 10), # expectedMin/Max must cause 10 values
(500000, 2, 'cF', 300, 2, 11), # expectedMin/Max must cause 10 values
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
for (rowCount, colCount, hex_key, timeoutSecs, expectedMin, expectedMax) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
legalValues = {}
for x in range(expectedMin, expectedMax):
legalValues[x] = x
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
h2o.beta_features = False
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=10, doSummary=False)
print csvFilename, 'parse time:', parseResult['response']['time']
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
h2o.beta_features = True
summaryResult = h2o_cmd.runSummary(key=hex_key, cols=0, max_ncols=1)
if h2o.verbose:
print "summaryResult:", h2o.dump_json(summaryResult)
summaries = summaryResult['summaries']
scipyCol = 0
for column in summaries:
colname = column['colname']
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
pctile = stats['pctile']
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hcnt:
e = .1 * rowCount
self.assertAlmostEqual(b, .1 * rowCount, delta=.01*rowCount,
msg="Bins not right. b: %s e: %s" % (b, e))
print "pctile:", pctile
print "maxs:", maxs
self.assertEqual(maxs[0], expectedMax)
print "mins:", mins
self.assertEqual(mins[0], expectedMin)
for v in pctile:
self.assertTrue(v >= expectedMin,
"Percentile value %s should all be >= the min dataset value %s" % (v, expectedMin))
self.assertTrue(v <= expectedMax,
"Percentile value %s should all be <= the max dataset value %s" % (v, expectedMax))
eV1 = [1.0, 1.0, 1.0, 3.0, 4.0, 5.0, 7.0, 8.0, 9.0, 10.0, 10.0]
if expectedMin==1:
eV = eV1
elif expectedMin==0:
eV = [e-1 for e in eV1]
elif expectedMin==2:
eV = [e+1 for e in eV1]
else:
raise Exception("Test doesn't have the expected percentileValues for expectedMin: %s" % expectedMin)
trial += 1
# if colname!='' and expected[scipyCol]:
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
scipyCol += 1
def test_summary2_uniform(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, 1, 'x.hex', 0.0, 20000.0,
['C1', 0, 5000.0, 10000.0, 15000.0, 20000.0]),
(ROWS, 1, 'x.hex', -5000.0, 0.0,
['C1', -5000.0, -3750.0, -2500.0, -1250.0, 0.0]),
(ROWS, 1, 'x.hex', -100000.0, 100000.0,
['C1', -100000.0, -50000.0, 0.0, 50000.0, 100000.0]),
(ROWS, 1, 'x.hex', -1.0, 1.0, ['C1', -1.0, -0.50, 0.0, 0.50, 1.0]),
(ROWS, 1, 'A.hex', 1.0, 100.0,
['C1', 1.0, 26.0, 51.0, 76.0, 100.0]),
(ROWS, 1, 'A.hex', -99.0, 99.0,
['C1', -99.0, -50.0, 0.0, 50.0, 99.0]),
(ROWS, 1, 'B.hex', 1.0, 10000.0,
['C1', 1.0, 2501.0, 5001.0, 7501.0, 10000.0]),
(ROWS, 1, 'B.hex', -100.0, 100.0,
['C1', -100.0, -50.0, 0.0, 50.0, 100.0]),
(ROWS, 1, 'C.hex', 1.0, 100000.0,
['C1', 1.0, 25001.0, 50001.0, 75001.0, 100000.0]),
(ROWS, 1, 'C.hex', -100.0, 100.0,
['C1', -100.0, -50.0, 0.0, 50.0, 100.0]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax,
expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
(actualMax, actualMin) = write_syn_dataset(csvPathname, rowCount,
colCount, expectedMin,
expectedMax,
SEEDPERFILE)
# adjust the min/max depending on what the min/max actually was!
# the expected 25%/50%/75% will still be off
expected[1] = actualMin
expected[5] = actualMax
# max error = half the bin size?
# use this for comparing to sklearn/sort
expectedRange = expectedMax - expectedMin
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange / (MAX_QBINS - 2)
maxDelta = 1 * expectedBin
# how much error do we get in the random distribution gen? pain. It's a probability issue
# smaller error likely with larger # of values.
# the maxDelta used for the scipy/sort compare can be tighter, since it's looking
# at actual data
# this is way too coarse. can't get the distribution tight?
maxDeltaPlusDistVariance = 10 * maxDelta
# allow some fuzz in the comparison to scipy/sort
maxDelta = 1.1 * maxDelta
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key,
max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
quantile = 0.5 if DO_MEDIAN else .999
# get both answers since we feed both below for checking
q = h2o.nodes[0].quantiles(source_key=hex_key,
column=column['colname'],
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=2,
interpolation_type=7) # linear
qresult = q['result']
qresult_single = q['result_single']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", q['iterations'])
h2p.blue_print("h2o quantiles interpolated:", q['interpolated'])
print h2o.dump_json(q)
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
zeros = stats['zeros']
mins = stats['mins']
# these should match exactly except for fp compare error?
h2o_util.assertApproxEqual(mins[0],
expected[1],
rel=.00001,
msg='min is not expected')
maxs = stats['maxs']
h2o_util.assertApproxEqual(maxs[0],
expected[5],
rel=.00001,
msg='max is not expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [
0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99
]
pctile = stats['pctile']
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxDeltaPlusDistVariance,
msg='25th percentile is not approx. expected for generated uniform range %s %s' %\
(expectedMin, expectedMax))
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxDeltaPlusDistVariance,
msg='50th percentile is not approx. expected for generated uniform range %s %s' %\
(expectedMin, expectedMax))
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxDeltaPlusDistVariance,
msg='75th percentile is not approx. expected for generated uniform range %s %s' %\
(expectedMin, expectedMax))
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
# too hard to estimate when there are ints now, due to floor/ceil int alignment?
# don't check the last two bins
for b in hcnt[1:(-2 if len(hcnt) > 2 else -1)]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(hcnt)
self.assertAlmostEqual(b,
rowCount / len(hcnt),
delta=.01 * rowCount,
msg="Bins not right. b: %s e: %s" %
(b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname,
"(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname != '':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxDelta,
)
h2o.nodes[0].remove_all_keys()
def test_summary2_uniform_int_w_NA(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
M = 100
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, 1, 'B.hex', 1, 1000 * M,
('C1', 1.0 * M, 250.0 * M, 500.0 * M, 750.0 * M, 1000.0 * M)),
(ROWS, 1, 'B.hex', 1, 1000, ('C1', 1.0, 250.0, 500.0, 750.0,
1000.0)),
(ROWS, 1, 'x.hex', 1, 20000, ('C1', 1.0, 5000.0, 10000.0, 15000.0,
20000.0)),
(ROWS, 1, 'x.hex', -5000, 0, ('C1', -5000.00, -3750.0, -2500.0,
-1250.0, 0)),
(ROWS, 1, 'x.hex', -100000, 100000, ('C1', -100000.0, -50000.0, 0,
50000.0, 100000.0)),
# (ROWS, 1, 'A.hex', 1, 101, ('C1', 1.0, 26.00, 51.00, 76.00, 101.0)),
# (ROWS, 1, 'A.hex', -99, 99, ('C1', -99, -49.0, 0, 49.00, 99)),
(ROWS, 1, 'B.hex', 1, 10000, ('C1', 1.0, 2501.0, 5001.0, 7501.0,
10000.0)),
(ROWS, 1, 'B.hex', -100, 100, ('C1', -100.0, -50.0, 0.0, 50.0,
100.0)),
(ROWS, 1, 'C.hex', 1, 100000, ('C1', 1.0, 25001.0, 50001.0,
75001.0, 100000.0)),
# (ROWS, 1, 'C.hex', -101, 101, ('C1', -101, -51, -1, 49.0, 100.0)),
]
if not DO_REAL:
# only 3 integer values!
tryList.append(\
(1000000, 1, 'x.hex', -1, 1, ('C1', -1.0, -1, 0.000, 1, 1.00)) \
)
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax,
expected) in tryList:
# max error = half the bin size?
maxDelta = ((expectedMax - expectedMin) / (MAX_QBINS + 0.0))
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
# also need to add some variance due to random distribution?
# maybe a percentage of the mean
distMean = (expectedMax - expectedMin) / 2
maxShift = distMean * .01
maxDelta = maxDelta + maxShift
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin,
expectedMax, SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=60,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key,
max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
h2o_util.assertApproxEqual(mins[0],
expected[1],
tol=maxDelta,
msg='min is not approx. expected')
h2o_util.assertApproxEqual(maxs[0],
expected[5],
tol=maxDelta,
msg='max is not approx. expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [
0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99
]
pctile = stats['pctile']
h2o_util.assertApproxEqual(
pctile[3],
expected[2],
tol=maxDelta,
msg='25th percentile is not approx. expected')
h2o_util.assertApproxEqual(
pctile[5],
expected[3],
tol=maxDelta,
msg='50th percentile (median) is not approx. expected')
h2o_util.assertApproxEqual(
pctile[7],
expected[4],
tol=maxDelta,
msg='75th percentile is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(
hcnt
) # expect 21 thresholds, so 20 bins. each 5% of rows (uniform distribution)
# don't check the edge bins
self.assertAlmostEqual(b,
rowCount / len(hcnt),
delta=.01 * rowCount,
msg="Bins not right. b: %s e: %s" %
(b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname,
"(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
scipyCol = 0
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname != '':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
h2o.nodes[0].remove_all_keys()
def test_summary2_percentile(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(100000, 1, 'cD', 300),
(100000, 2, 'cE', 300),
]
timeoutSecs = 10
trial = 1
for (rowCount, colCount, hex_key, timeoutSecs) in tryList:
print 'Trial:', trial
SEEDPERFILE = random.randint(0, sys.maxint)
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
legalValues = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10} # set. http://docs.python.org/2/library/stdtypes.html#set
expectedMin = min(legalValues)
expectedMax = max(legalValues)
expectedUnique = (expectedMax - expectedMin) + 1
mode = 0.5 # rounding to nearest int will shift us from this for expected mean
expectedMean = 0.5
expectedSigma = 0.5
write_syn_dataset(csvPathname, rowCount, colCount,
low=expectedMin, high=expectedMax, mode=mode,
SEED=SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename('.', csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key,
timeoutSecs=10, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
summaryResult = h2o_cmd.runSummary(key=hex_key)
if h2o.verbose:
print "summaryResult:", h2o.dump_json(summaryResult)
summaries = summaryResult['summaries']
scipyCol = 0
for column in summaries:
colname = column['colname']
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
pctile = stats['pctile']
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hbrk:
self.assertIn(int(b), legalValues)
self.assertEqual(len(hbrk), len(legalValues))
# self.assertAlmostEqual(hcnt[0], 0.5 * rowCount, delta=.01*rowCount)
# self.assertAlmostEqual(hcnt[1], 0.5 * rowCount, delta=.01*rowCount)
print "pctile:", pctile
print "maxs:", maxs
# we round to int, so we may introduce up to 0.5 rounding error? compared to "mode" target
self.assertAlmostEqual(maxs[0], expectedMax, delta=0.01)
print "mins:", mins
self.assertAlmostEqual(mins[0], expectedMin, delta=0.01)
for v in pctile:
self.assertTrue(v >= expectedMin,
"Percentile value %s should all be >= the min dataset value %s" % (v, expectedMin))
self.assertTrue(v <= expectedMax,
"Percentile value %s should all be <= the max dataset value %s" % (v, expectedMax))
eV1 = [1.0, 1.0, 1.0, 3.0, 4.0, 5.0, 7.0, 8.0, 9.0, 10.0, 10.0]
if expectedMin==1:
eV = eV1
elif expectedMin==0:
eV = [e-1 for e in eV1]
elif expectedMin==2:
eV = [e+1 for e in eV1]
else:
raise Exception("Test doesn't have the expected percentileValues for expectedMin: %s" % expectedMin)
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
scipyCol += 1
def test_summary2_uniform(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(ROWS, 1, 'x.hex', 0.0, 20000.0, ['C1', 0, 5000.0, 10000.0, 15000.0, 20000.0]),
(ROWS, 1, 'x.hex', -5000.0, 0.0, ['C1', -5000.0, -3750.0, -2550.0, -1250.0, 0.0]),
(ROWS, 1, 'x.hex', -100000.0, 100000.0, ['C1', -100000.0, -50000.0, 0.0, 50000.0, 100000.0]),
(ROWS, 1, 'x.hex', -1.0, 1.0, ['C1', -1.0, -0.50, 0.0, 0.50, 1.0]),
(ROWS, 1, 'A.hex', 1.0, 100.0, ['C1', 1.0, 26.0, 51.0, 76.0, 100.0]),
(ROWS, 1, 'A.hex', -99.0, 99.0, ['C1', -99.0, -50.0, 0.0, 50.0, 99.0]),
(ROWS, 1, 'B.hex', 1.0, 10000.0, ['C1', 1.0, 2501.0, 5001.0, 7501.0, 10000.0]),
(ROWS, 1, 'B.hex', -100.0, 100.0, ['C1', -100.0, -50.0, 0.0, 50.0, 100.0]),
(ROWS, 1, 'C.hex', 1.0, 100000.0, ['C1', 1.0, 25001.0, 50001.0, 75001.0, 100000.0]),
(ROWS, 1, 'C.hex', -100.0, 100.0, ['C1', -100.0, -50.0, 0.0, 50.0, 100.0]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
(actualMax, actualMin) = write_syn_dataset(csvPathname, rowCount, colCount,
expectedMin, expectedMax, SEEDPERFILE)
# adjust the min/max depending on what the min/max actually was!
# the expected 25%/50%/75% will still be off
expected[1] = actualMin
expected[5] = actualMax
# max error = half the bin size?
# use this for comparing to sklearn/sort
expectedRange = expectedMax - expectedMin
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange/(MAX_QBINS-2)
maxDelta = 0.5 * expectedBin
# how much error do we get in the random distribution gen? pain. It's a probability issue
# smaller error likely with larger # of values.
# the maxDelta used for the scipy/sort compare can be tighter, since it's looking
# at actual data
# this is way too coarse. can't get the distribution tight?
maxDeltaPlusDistVariance = 10 * maxDelta
# allow some fuzz in the comparison to scipy/sort
maxDelta = 1.1 * maxDelta
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key,
timeoutSecs=30, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
quantile = 0.5 if DO_MEDIAN else .999
# get both answers since we feed both below for checking
q = h2o.nodes[0].quantiles(source_key=hex_key, column=column['colname'],
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=2, interpolation_type=7) # linear
qresult = q['result']
qresult_single = q['result_single']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", q['iterations'])
h2p.blue_print("h2o quantiles interpolated:", q['interpolated'])
print h2o.dump_json(q)
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats['zeros']
mins = stats['mins']
# these should match exactly except for fp compare error?
h2o_util.assertApproxEqual(mins[0], expected[1], rel=.00001, msg='min is not expected')
maxs = stats['maxs']
h2o_util.assertApproxEqual(maxs[0], expected[5], rel=.00001, msg='max is not expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct= [0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99]
pctile = stats['pctile']
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxDeltaPlusDistVariance,
msg='25th percentile is not approx. expected for generated uniform range %s %s' %\
(expectedMin, expectedMax))
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxDeltaPlusDistVariance,
msg='50th percentile is not approx. expected for generated uniform range %s %s' %\
(expectedMin, expectedMax))
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxDeltaPlusDistVariance,
msg='75th percentile is not approx. expected for generated uniform range %s %s' %\
(expectedMin, expectedMax))
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
# too hard to estimate when there are ints now, due to floor/ceil int alignment?
# don't check the last two bins
for b in hcnt[1:(-2 if len(hcnt)>2 else -1)]:
# should we be able to check for a uniform distribution in the files?
e = numRows/len(hcnt)
self.assertAlmostEqual(b, rowCount/len(hcnt), delta=.01*rowCount,
msg="Bins not right. b: %s e: %s" % (b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname, "(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxDelta,
)
h2o.nodes[0].remove_all_keys()
def test_summary_stepping(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# co.label, (min, 25th, 50th, 75th, max)
# parse setup error
# (1, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -5000, 0, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', .4900, .5000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -.5000, -.4900, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 490, 500, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -500, -490, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 49000, 50000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -50000, -49000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 4900, 5000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -5000, -4900, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -100000, 100000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', -1, 1, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 100, ['C1', None, None, None, None, None]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, rangeMin, rangeMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
(expectedMin, expectedMax) = write_syn_dataset(csvPathname, rowCount, colCount, rangeMin, rangeMax, SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
# add 5% for fp errors?
maxErr = ((expectedMax - expectedMin)/1000) * 1.05
expected[1] = expectedMin
expected[5] = expectedMax
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=30, doSummary=False)
pA = h2o_cmd.ParseObj(parseResult, expectedNumRows=rowCount, expectedNumCols=colCount)
print pA.numRows, pA.numCols, pA.parse_key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount, expectedNumCols=colCount, expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
# column 0 not used here
assert len(expected) == 6
co = h2o_cmd.runSummary(key=hex_key, column=0, expected=expected[1:], maxDelta=maxErr)
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
print "maxErr", maxErr
if co.label!='' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=False,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.99,
h2oSummary2=co.percentiles[5 if DO_MEDIAN else 9],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
def test_summary2_exp(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# co.label, (min, 25th, 50th, 75th, max)
# parse setup error
# (1, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
# (10, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
# (100, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
# (1000, 1, 'x.hex', -5000, 0, ['C1', None, None, None, None, None]),
# (10000, 1, 'x.hex', -100000, 100000, ['C1', None, None, None, None, None]),
# (100000, 1, 'x.hex', -1, 1, ['C1', None, None, None, None, None]),
# (1000000, 1, 'A.hex', 1, 100, ['C1', None, None, None, None, None]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
class Column(object):
def __init__(self, column):
assert isinstance(column, dict)
for k,v in column.iteritems():
setattr(self, k, v) # achieves self.k = v
for (rowCount, colCount, hex_key, rangeMin, rangeMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(csvPathname,
rowCount, colCount, lambd=LAMBD, SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxDelta = ((expectedMax - expectedMin)/20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
expected[1] = expectedMin
expected[5] = expectedMax
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put',
hex_key=hex_key, timeoutSecs=30, doSummary=False)
numRows, numCols, parse_key = h2o_cmd.infoFromParse(parseResult)
inspect = h2o_cmd.runInspect(key=parse_key)
missingList, labelList, numRows, numCols = h2o_cmd.infoFromInspect(inspect)
print "\n" + csvFilename
# column 0?
summaryResult = h2o_cmd.runSummary(key=hex_key, column='C1')
h2o.verboseprint("Summary2 summaryResult:", h2o.dump_json(summaryResult))
# default_pctiles
# isText
# rows
# off
# key
# checksum
# only one column
columns = summaryResult['frames'][0]['columns']
default_pctiles = summaryResult['frames'][0]['default_pctiles']
co = Column(columns[0])
# how are enums binned. Stride of 1? (what about domain values)
coList = [
co.base,
len(co.bins),
len(co.data),
co.domain,
co.label,
co.maxs,
co.mean,
co.mins,
co.missing,
co.ninfs,
co.pctiles,
co.pinfs,
co.precision,
co.sigma,
co.str_data,
co.stride,
co.type,
co.zeros,
]
for c in coList:
print c
print "len(co.bins):", len(co.bins)
print "co.label:", co.label, "mean (2 places):", h2o_util.twoDecimals(co.mean)
# what is precision. -1?
print "co.label:", co.label, "std dev. (2 places):", h2o_util.twoDecimals(co.sigma)
print "FIX! hacking the co.pctiles because it's short by two"
pctiles = [0] + co.pctiles + [0]
# the thresholds h2o used, should match what we expected
if expected[0]:
self.assertEqual(co.label, expected[0])
if expected[1]:
h2o_util.assertApproxEqual(co.mins[0], expected[1], tol=maxDelta, msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(pctiles[3], expected[2], tol=maxDelta, msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(pctiles[5], expected[3], tol=maxDelta, msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(pctiles[7], expected[4], tol=maxDelta, msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(co.maxs[0], expected[5], tol=maxDelta, msg='max is not approx. expected')
# figure out the expected max error
# use this for comparing to sklearn/sort
if expected[1] and expected[5]:
expectedRange = expected[5] - expected[1]
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange/(MAX_QBINS-2)
maxErr = expectedBin # should we have some fuzz for fp?
else:
print "Test won't calculate max expected error"
maxErr = 0
pt = h2o_util.twoDecimals(pctiles)
mx = h2o_util.twoDecimals(co.maxs)
mn = h2o_util.twoDecimals(co.mins)
print "co.label:", co.label, "co.pctiles (2 places):", pt
print "default_pctiles:", default_pctiles
print "co.label:", co.label, "co.maxs: (2 places):", mx
print "co.label:", co.label, "co.mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max co.label:", co.label, "(2 places):", compareActual)
print "co.label:", co.label, "co.maxs (2 places):", mx
print "co.label:", co.label, "co.mins (2 places):", mn
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
print "h2oSummary2MaxErr", maxErr
if co.label!='' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=False,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctiles[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
def test_summary2_unifiles(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
# new with 1000 bins. copy expected from R
tryList = [
(
'cars.csv',
'c.hex',
[
(None, None, None, None, None, None),
('economy (mpg)', None, None, None, None, None),
('cylinders', None, None, None, None, None),
],
),
(
'runifA.csv',
'A.hex',
[
(None, 1.00, 25.00, 50.00, 75.00, 100.0),
('x', -99.9, -44.7, 8.26, 58.00, 91.7),
],
),
# colname, (min, 25th, 50th, 75th, max)
(
'runif.csv',
'x.hex',
[
(None, 1.00, 5000.0, 10000.0, 15000.0, 20000.00),
('D', -5000.00, -3735.0, -2443, -1187.0, 99.8),
('E', -100000.0, -49208.0, 1783.8, 50621.9, 100000.0),
('F', -1.00, -0.4886, 0.00868, 0.5048, 1.00),
],
),
(
'runifB.csv',
'B.hex',
[
(None, 1.00, 2501.00, 5001.00, 7501.00, 10000.00),
('x', -100.00, -50.1, 0.974, 51.7, 100, 00),
],
),
(
'runifC.csv',
'C.hex',
[
(None, 1.00, 25002.00, 50002.00, 75002.00, 100000.00),
('x', -100.00, -50.45, -1.135, 49.28, 100.00),
],
),
]
timeoutSecs = 15
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
timeoutSecs = 60
for (csvFilename, hex_key, expectedCols) in tryList:
csvPathname = csvFilename
csvPathnameFull = h2i.find_folder_and_filename('smalldata',
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(bucket='smalldata',
path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=10,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
# okay to get more cols than we want
# okay to vary MAX_QBINS because we adjust the expected accuracy
summaryResult = h2o_cmd.runSummary(key=hex_key,
max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
summaries = summaryResult['summaries']
scipyCol = 0
for expected, column in zip(expectedCols, summaries):
colname = column['colname']
if expected[0]:
self.assertEqual(colname,
expected[0]), colname, expected[0]
else:
# if the colname is None, skip it (so we don't barf on strings on the h2o quantile page
scipyCol += 1
continue
quantile = 0.5 if DO_MEDIAN else .999
# h2o has problem if a list of columns (or dictionary) is passed to 'column' param
q = h2o.nodes[0].quantiles(
source_key=hex_key,
column=column['colname'],
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=2,
interpolation_type=7) # for comparing to summary2
qresult = q['result']
qresult_single = q['result_single']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", q['iterations'])
h2p.blue_print("h2o quantiles interpolated:",
q['interpolated'])
print h2o.dump_json(q)
# ('', '1.00', '25002.00', '50002.00', '75002.00', '100000.00'),
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
print stattype
# FIX! we should compare mean and sd to expected?
# enums don't have mean or sd?
if stattype != 'Enum':
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
pct = stats['pct']
print "pct:", pct
print ""
# the thresholds h2o used, should match what we expected
expectedPct = [
0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9,
0.95, 0.99
]
pctile = stats['pctile']
# figure out the expected max error
# use this for comparing to sklearn/sort
if expected[1] and expected[5]:
expectedRange = expected[5] - expected[1]
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange / (MAX_QBINS - 2)
maxErr = 0.5 * expectedBin # should we have some fuzz for fp?
else:
print "Test won't calculate max expected error"
maxErr = 0
# hack..assume just one None is enough to ignore for cars.csv
if expected[1]:
h2o_util.assertApproxEqual(
mins[0],
expected[1],
tol=maxErr,
msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(
pctile[3],
expected[2],
tol=maxErr,
msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(
pctile[5],
expected[3],
tol=maxErr,
msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(
pctile[7],
expected[4],
tol=maxErr,
msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(
maxs[0],
expected[5],
tol=maxErr,
msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hcnt:
# should we be able to check for a uniform distribution in the files?
e = .1 * numRows
# self.assertAlmostEqual(b, .1 * rowCount, delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
if stattype != 'Enum':
pt = h2o_util.twoDecimals(pctile)
print "colname:", colname, "pctile (2 places):", pt
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
actual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", actual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
# need to ignore the car names
if colname != '' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
# FIX! ignore for now
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
if False and h2o_util.approxEqual(pctile[5],
0.990238116744,
tol=0.002,
msg='stop here'):
raise Exception("stopping to look")
scipyCol += 1
trial += 1
def test_quant_cols(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
if getpass.getuser() == 'kevin':
tryList = [
(None, '/home/kevin/Downloads/t.csv', 15, 11, 'cE', 300),
('home-0xdiag-datasets', 'airlines/year2013.csv', None, None,
'cE', 300),
]
else:
tryList = [
('home-0xdiag-datasets', 'airlines/year2013.csv', None, None,
'cE', 300),
]
# h2b.browseTheCloud()
trial = 0
for (bucket, csvPathname, iColCount, oColCount, hex_key,
timeoutSecs) in tryList:
xList = []
eList = []
fList = []
# PARSE*******************************************************
parseResult = h2i.import_parse(bucket=bucket,
path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=200,
doSummary=False)
csvPathnameFull = h2i.find_folder_and_filename(bucket,
csvPathname,
returnFullPath=True)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
numRows = inspect['numRows']
numCols = inspect['numCols']
if not oColCount:
iColCount = 0
if not oColCount:
oColCount = numCols
colCount = iColCount + oColCount
for i in range(0, numCols):
print "Column", i, "summary"
h2o_cmd.runSummary(key=hex_key, max_qbins=1, cols=i)
# print h2o.dump_json(inspect)
levels = h2o.nodes[0].levels(source=hex_key)
# print "levels result:", h2o.dump_json(levels)
(missingValuesDict, constantValuesDict, enumSizeDict, colTypeDict, colNameDict) = \
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'], exceptionOnMissingValues=False)
# error if any col has constant values
if len(constantValuesDict) != 0:
# raise Exception("Probably got a col NA'ed and constant values as a result %s" % constantValuesDict)
print "Probably got a col NA'ed and constant values as a result %s" % constantValuesDict
# start after the last input col
levels = h2o.nodes[0].levels(source=hex_key)
l = levels['levels']
for column in range(iColCount, iColCount + oColCount):
if l[column]:
print "Skipping", column, "because it's enum (says levels)"
continue
# QUANTILE*******************************************************
quantile = 0.5 if DO_MEDIAN else .999
# first output col. always fed by an exec cut, so 0?
start = time.time()
# file has headers. use col index
q = h2o.nodes[0].quantiles(source_key=hex_key,
column=column,
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=1)
qresult = q['result']
h2p.red_print("result:", q['result'], "quantile", quantile,
"interpolated:", q['interpolated'], "iterations",
q['iterations'])
elapsed = time.time() - start
print "quantile end on ", hex_key, 'took', elapsed, 'seconds.'
quantileTime = elapsed
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
if 1 == 1:
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=column, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
# h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
use_genfromtxt=True,
)
trial += 1
execTime = 0
xList.append(column)
eList.append(execTime)
fList.append(quantileTime)
# remove all keys*******************************************************
# what about hex_key?
if 1 == 0:
start = time.time()
h2o.nodes[0].remove_all_keys()
elapsed = time.time() - start
print "remove all keys end on took", elapsed, 'seconds.'
#****************************************************************
# PLOTS. look for eplot.jpg and fplot.jpg in local dir?
if DO_PLOT:
xLabel = 'column (0 is first)'
eLabel = 'exec cut time'
fLabel = 'quantile time'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList,
xLabel,
eListTitle,
eList,
eLabel,
fListTitle,
fList,
fLabel,
server=True)
def test_summary2_exp(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(10, 1, 'x.hex', 1, 20000, ('C1', None, None, None, None, None)),
(100, 1, 'x.hex', 1, 20000, ('C1', None, None, None, None, None)),
(1000, 1, 'x.hex', -5000, 0, ('C1', None, None, None, None, None)),
(10000, 1, 'x.hex', -100000, 100000, ('C1', None, None, None, None,
None)),
(100000, 1, 'x.hex', -1, 1, ('C1', None, None, None, None, None)),
(1000000, 1, 'A.hex', 1, 100, ('C1', None, None, None, None,
None)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
# rangeMin and rangeMax are not used right now
for (rowCount, colCount, hex_key, rangeMin, rangeMax,
expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(csvPathname,
rowCount,
colCount,
lambd=LAMBD,
SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxDelta = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key,
max_qbins=MAX_QBINS)
h2o.verboseprint("Summary2 summaryResult:",
h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
expectedPct = [
0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99
]
pctile = stats['pctile']
# the thresholds h2o used, should match what we expected
if expected[0]:
self.assertEqual(colname, expected[0])
if expected[1]:
h2o_util.assertApproxEqual(mins[0],
expected[1],
tol=maxDelta,
msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(
pctile[3],
expected[2],
tol=maxDelta,
msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(
pctile[5],
expected[3],
tol=maxDelta,
msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(
pctile[7],
expected[4],
tol=maxDelta,
msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(maxs[0],
expected[5],
tol=maxDelta,
msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print ""
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
print "Can't estimate the bin distribution"
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname,
"(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
if colname != '' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
)
def test_quant_cmp_uniform(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
(5 * ROWS, 1, 'x.hex', 1, 20000,
['C1', 1.10, 5000.0, 10000.0, 15000.0, 20000.00]),
(5 * ROWS, 1, 'x.hex', -5000, 0,
['C1', -5001.00, -3750.0, -2445, -1200.0, 99]),
(1 * ROWS, 1, 'x.hex', -100000, 100000,
['C1', -100001.0, -50000.0, 1613.0, 50000.0, 100000.0]),
(1 * ROWS, 1, 'x.hex', -1, 1,
['C1', -1.05, -0.48, 0.0087, 0.50, 1.00]),
(1 * ROWS, 1, 'A.hex', 1, 100,
['C1', 1.05, 26.00, 51.00, 76.00, 100.0]),
(1 * ROWS, 1, 'A.hex', -99, 99, ['C1', -99, -50.0, 0, 50.00, 99]),
(1 * ROWS, 1, 'B.hex', 1, 10000,
['C1', 1.05, 2501.00, 5001.00, 7501.00, 10000.00]),
(1 * ROWS, 1, 'B.hex', -100, 100,
['C1', -100.10, -50.0, 0.85, 51.7, 100, 00]),
(1 * ROWS, 1, 'C.hex', 1, 100000,
['C1', 1.05, 25002.00, 50002.00, 75002.00, 100000.00]),
(1 * ROWS, 1, 'C.hex', -101, 101,
['C1', -100.10, -50.45, -1.18, 49.28, 100.00]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax,
expected) in tryList:
# max error = half the bin size?
colname = expected[0]
maxDelta = ((expectedMax - expectedMin) / 1000.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin,
expectedMax, SEEDPERFILE)
# need the full pathname when python parses the csv for numpy/sort
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
#***************************
# Parse
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30,
doSummary=False)
pA = h2o_cmd.ParseObj(parseResult,
expectedNumRows=rowCount,
expectedNumCols=colCount)
numRows = pA.numRows
numCols = pA.numCols
parse_key = pA.parse_key
# this guy can take json object as first thing, or re-read with key
iA = h2o_cmd.InspectObj(parse_key,
expectedNumRows=rowCount,
expectedNumCols=colCount,
expectedMissinglist=[])
#***************************
# Summary
co = h2o_cmd.runSummary(key=parse_key)
default_pctiles = co.default_pctiles
coList = [
co.base,
len(co.bins),
len(co.data), co.domain, co.label, co.maxs, co.mean, co.mins,
co.missing, co.ninfs, co.pctiles, co.pinfs, co.precision,
co.sigma, co.str_data, co.stride, co.type, co.zeros
]
for c in coList:
print c
print "len(co.bins):", len(co.bins)
print "co.label:", co.label, "mean (2 places):", h2o_util.twoDecimals(
co.mean)
print "co.label:", co.label, "std dev. (2 places):", h2o_util.twoDecimals(
co.sigma)
print "FIX! hacking the co.pctiles because it's short by two"
summ_pctiles = [0] + co.pctiles + [0]
pt = h2o_util.twoDecimals(summ_pctiles)
mx = h2o_util.twoDecimals(co.maxs)
mn = h2o_util.twoDecimals(co.mins)
exp = h2o_util.twoDecimals(expected[1:])
print "co.label:", co.label, "co.pctiles (2 places):", pt
print "default_pctiles:", default_pctiles
print "co.label:", co.label, "co.maxs: (2 places):", mx
print "co.label:", co.label, "co.mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
h2p.green_print("min/25/50/75/max co.label:", co.label, "(2 places):",\
mn[0], pt[3], pt[5], pt[7], mx[0])
h2p.green_print("min/25/50/75/max co.label:", co.label, "(2 places):",\
exp[0], exp[1], exp[2], exp[3], exp[4])
#***************************
# Quantile
# the thresholds h2o used, should match what we expected
# using + here seems to result in an odd tuple..doesn't look right to h2o param
# so went with this. Could add '[' and ']' to the list first, before the join.
probsStr = "[%s]" % ",".join(map(str, probsList))
parameters = {
'model_id': "a.hex",
'training_frame': parse_key,
'validation_frame': parse_key,
'ignored_columns': None,
'probs': probsStr,
}
model_key = 'qhex'
bmResult = h2o.n0.build_model(algo='quantile',
model_id=model_key,
training_frame=parse_key,
parameters=parameters,
timeoutSecs=10)
bm = OutputObj(bmResult, 'bm')
msec = bm.jobs[0]['msec']
print "bm msec", msec
# quantile result is just a job result to a key
modelResult = h2o.n0.models(key=model_key)
model = OutputObj(modelResult['models'][0], 'model')
print "model.output:", model.output
print "model.output:['quantiles']", model.output['quantiles']
print "model.output:['iterations']", model.output['iterations']
print "model.output:['names']", model.output['names']
quantiles = model.output['quantiles'][
0] # why is this a double array
iterations = model.output['iterations']
assert iterations == 11, iterations
print "quantiles: ", quantiles
print "iterations: ", iterations
# cmmResult = h2o.n0.compute_model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
# cmm = OutputObj(cmmResult, 'cmm')
# mmResult = h2o.n0.model_metrics(model=model_key, frame=parse_key, timeoutSecs=60)
# mm = OutputObj(mmResult, 'mm')
# prResult = h2o.n0.predict(model=model_key, frame=parse_key, timeoutSecs=60)
# pr = OutputObj(prResult['model_metrics'][0]['predictions'], 'pr')
h2o_cmd.runStoreView()
trial += 1
# compare the last threshold
if colname != '':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=CHECK_PCTILE,
# h2oSummary2=pctile[-1],
# h2oQuantilesApprox=result, # from exec
h2oExecQuantiles=quantiles[CHECK_PCTILE_INDEX],
)
h2o.nodes[0].remove_all_keys()
def test_summary2_unifiles2(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
# new with 1000 bins. copy expected from R
tryList = [
# colname, (min, 25th, 50th, 75th, max)
('breadth.csv', 'b.hex', False, [ ('C1', None, None, None, None, None)], 'smalldata', 'quantiles'),
# ('wonkysummary.csv', 'b.hex', False, [ ('X1', 7, 22, 876713, 100008, 1000046)], 'smalldata', None),
('wonkysummary.csv', 'b.hex', True, [ ('X1', None, None, None, None, None)], 'smalldata', None),
('covtype.data', 'c.hex', False, [ ('C1', None, None, None, None, None)], 'home-0xdiag-datasets', 'standard'),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (csvFilename, hex_key, skipHeader, expectedCols, bucket, pathPrefix) in tryList:
h2o.beta_features = False
if pathPrefix:
csvPathname = pathPrefix + "/" + csvFilename
else:
csvPathname = csvFilename
csvPathnameFull = h2i.find_folder_and_filename(bucket, csvPathname, returnFullPath=True)
if skipHeader:
header = 1
else:
header = 0
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname,
schema='put', header=header, hex_key=hex_key, timeoutSecs=10, doSummary=False)
print csvFilename, 'parse time:', parseResult['response']['time']
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["num_rows"]
numCols = inspect["num_cols"]
h2o.beta_features = True
# okay to get more cols than we want
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
summaries = summaryResult['summaries']
scipyCol = 0
for expected, column in zip(expectedCols, summaries):
colname = column['colname']
if expected[0]:
self.assertEqual(colname, expected[0])
quantile = 0.5 if DO_MEDIAN else OTHER_Q
q = h2o.nodes[0].quantiles(source_key=hex_key, column=scipyCol,
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=2)
qresult = q['result']
qresult_single = q['result_single']
qresult_iterations = q['iterations']
qresult_interpolated = q['interpolated']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", qresult_iterations)
h2p.blue_print("h2o quantiles interpolated:", qresult_interpolated)
print h2o.dump_json(q)
self.assertLess(qresult_iterations, 16,
msg="h2o does max of 16 iterations. likely no result_single if we hit max. is bins=1?")
# ('', '1.00', '25002.00', '50002.00', '75002.00', '100000.00'),
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
print stattype
# FIX! we should compare mean and sd to expected?
# enums don't have mean or sd?
if stattype!='Enum':
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
pct = stats['pct']
print "pct:", pct
print ""
# the thresholds h2o used, should match what we expected
pctile = stats['pctile']
# hack..assume just one None is enough to ignore for cars.csv
if expected[1]:
h2o_util.assertApproxEqual(mins[0], expected[1], rel=0.02, msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(pctile[3], expected[2], rel=0.02, msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(pctile[5], expected[3], rel=0.02, msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(pctile[7], expected[4], rel=0.02, msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(maxs[0], expected[5], rel=0.02, msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hcnt:
# should we be able to check for a uniform distribution in the files?
e = .1 * numRows
# self.assertAlmostEqual(b, .1 * rowCount, delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
if stattype!='Enum':
pt = h2o_util.twoDecimals(pctile)
print "colname:", colname, "pctile (2 places):", pt
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
actual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", actual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
## ignore for blank colnames, issues with quoted numbers
# covtype is too big to do in scipy
if colname!='' and expected[scipyCol] and csvFilename!= 'covtype.data':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=skipHeader, # important!!
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else OTHER_Q,
h2oSummary2=pctile[5 if DO_MEDIAN else OTHER_Q_SUMM_INDEX],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
)
scipyCol += 1
trial += 1
def test_summary2_small(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
# if rowCount is None, we'll just use the data values
# None in expected values means no compare
(None, 1, 'x.hex', [-1, 0, 1], ('C1', None, None, 0, None, None)),
(None, 2, 'x.hex', [-1, 0, 1], ('C1', None, None, 0, None, None)),
(None, 10, 'x.hex', [-1, 0, 1], ('C1', None, None, 0, None, None)),
(None, 100, 'x.hex', [-1, 0,
1], ('C1', None, None, 0, None, None)),
(None, 1000, 'x.hex', [-1, 0,
1], ('C1', None, None, 0, None, None)),
# (None, 10000, 'x.hex', [-1,0,1], ('C1', None, None, 0, None, None)),
# (COLS, 1, 'x.hex', [1,0,-1], ('C1', None, None, None, None, None)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, values, expected) in tryList:
# max error = half the bin size?
expectedMax = max(values)
expectedMin = min(values)
maxDelta = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
# hmm...say we should be 100% accurate for these tests?
maxDelta = 0
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
if not rowCount:
rowFile = len(values)
else:
rowFile = rowCount
csvFilename = 'syn_' + "binary" + "_" + str(rowFile) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, values,
SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=30,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key,
max_qbins=MAX_QBINS,
timeoutSecs=45)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
quantile = 0.5 if DO_MEDIAN else .999
q = h2o.nodes[0].quantiles(source_key=hex_key,
column=0,
interpolation_type=7,
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=2)
qresult = q['result']
qresult_single = q['result_single']
qresult_iterations = q['iterations']
qresult_interpolated = q['interpolated']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", qresult_iterations)
h2p.blue_print("h2o quantiles interpolated:", qresult_interpolated)
print h2o.dump_json(q)
self.assertLess(
qresult_iterations,
16,
msg=
"h2o does max of 16 iterations. likely no result_single if we hit max. is bins=1?"
)
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [
0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99
]
pctile = stats['pctile']
print "pctile:", pctile
if expected[0]:
self.assertEqual(colname, expected[0])
if expected[1]:
h2o_util.assertApproxEqual(mins[0],
expected[1],
tol=maxDelta,
msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(
pctile[3],
expected[2],
tol=maxDelta,
msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(
pctile[5],
expected[3],
tol=maxDelta,
msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(
pctile[7],
expected[4],
tol=maxDelta,
msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(maxs[0],
expected[5],
tol=maxDelta,
msg='max is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print ""
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(
hcnt
) # expect 21 thresholds, so 20 bins. each 5% of rows (uniform distribution)
# don't check the edge bins
self.assertAlmostEqual(b,
numRows / len(hcnt),
delta=1 + .01 * numRows,
msg="Bins not right. b: %s e: %s" %
(b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname,
"(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname != '':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=scipyCol, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
)
def test_exec2_quant_cmp_uniform(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(500000, 1, 'x.hex', 1, 20000, ('C1', 1.10, 5000.0, 10000.0,
15000.0, 20000.00)),
(500000, 1, 'x.hex', -5000, 0, ('C1', -5001.00, -3750.0, -2445,
-1200.0, 99)),
(100000, 1, 'x.hex', -100000, 100000, ('C1', -100001.0, -50000.0,
1613.0, 50000.0, 100000.0)),
(100000, 1, 'x.hex', -1, 1, ('C1', -1.05, -0.48, 0.0087, 0.50,
1.00)),
(100000, 1, 'A.hex', 1, 100, ('C1', 1.05, 26.00, 51.00, 76.00,
100.0)),
(100000, 1, 'A.hex', -99, 99, ('C1', -99, -50.0, 0, 50.00, 99)),
(100000, 1, 'B.hex', 1, 10000, ('C1', 1.05, 2501.00, 5001.00,
7501.00, 10000.00)),
(100000, 1, 'B.hex', -100, 100, ('C1', -100.10, -50.0, 0.85, 51.7,
100, 00)),
(100000, 1, 'C.hex', 1, 100000, ('C1', 1.05, 25002.00, 50002.00,
75002.00, 100000.00)),
(100000, 1, 'C.hex', -101, 101, ('C1', -100.10, -50.45, -1.18,
49.28, 100.00)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax,
expected) in tryList:
# max error = half the bin size?
maxDelta = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(
colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin,
expectedMax, SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None,
csvPathname,
returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname,
schema='put',
hex_key=hex_key,
timeoutSecs=10,
doSummary=False)
print "Parse result['destination_key']:", parseResult[
'destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype = stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(
mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(
sd)
zeros = stats['zeros']
mins = stats['mins']
h2o_util.assertApproxEqual(mins[0],
expected[1],
tol=maxDelta,
msg='min is not approx. expected')
maxs = stats['maxs']
h2o_util.assertApproxEqual(maxs[0],
expected[5],
tol=maxDelta,
msg='max is not approx. expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [
0.001, 0.01, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.99, 0.999
]
pctile = stats['pctile']
h2o_util.assertApproxEqual(
pctile[3],
expected[2],
tol=maxDelta,
msg='25th percentile is not approx. expected')
h2o_util.assertApproxEqual(
pctile[5],
expected[3],
tol=maxDelta,
msg='50th percentile (median) is not approx. expected')
h2o_util.assertApproxEqual(
pctile[7],
expected[4],
tol=maxDelta,
msg='75th percentile is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(hcnt)
# apparently we're not able to estimate for these datasets
# self.assertAlmostEqual(b, rowCount/len(hcnt), delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", compareActual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2p.blue_print("\nTrying exec quantile")
# thresholds = "c(0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99)"
# do the equivalent exec quantile?
# execExpr = "quantile(%s[,1],%s);" % (hex_key, thresholds)
print "Comparing (two places) each of the summary2 threshold quantile results, to single exec quantile"
for i, threshold in enumerate(thresholds):
# FIX! do two of the same?..use same one for the 2nd
if i != 0:
# execExpr = "r2=c(1); r2=quantile(%s[,4],c(0,.05,0.3,0.55,0.7,0.95,0.99))" % hex_key
execExpr = "r2=c(1); r2=quantile(%s[,1], c(%s,%s));" % (
hex_key, threshold, threshold)
(resultExec, result) = h2e.exec_expr(execExpr=execExpr,
timeoutSecs=30)
h2p.green_print("\nresultExec: %s" %
h2o.dump_json(resultExec))
h2p.blue_print(
"\nthreshold: %.2f Exec quantile: %s Summary2: %s" %
(threshold, result, pt[i]))
if not result:
raise Exception(
"exec result: %s for quantile: %s is bad" %
(result, threshold))
h2o_util.assertApproxEqual(
result,
pctile[i],
tol=maxDelta,
msg=
'exec percentile: %s too different from expected: %s' %
(result, pctile[i]))
# for now, do one with all, but no checking
else:
# This seemed to "work" but how do I get the key name for the list of values returned
# the browser result field seemed right, but nulls in the key
if 1 == 0:
execExpr = "r2=c(1); r2=quantile(%s[,1], c(%s));" % (
hex_key, ",".join(map(str, thresholds)))
else:
# does this way work (column getting)j
execExpr = "r2=c(1); r2=quantile(%s$C1, c(%s));" % (
hex_key, ",".join(map(str, thresholds)))
(resultExec, result) = h2e.exec_expr(execExpr=execExpr,
timeoutSecs=30)
inspect = h2o_cmd.runInspect(key='r2')
numCols = inspect['numCols']
numRows = inspect['numRows']
self.assertEqual(numCols, 1)
self.assertEqual(numRows, len(thresholds))
# FIX! should run thru the values in the col? how to get
# compare the last one
if colname != '':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=thresholds[-1],
# h2oSummary2=pctile[-1],
# h2oQuantilesApprox=result, # from exec
h2oExecQuantiles=result,
)
h2o.nodes[0].remove_all_keys()
def test_quant_cols(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
if getpass.getuser()=='kevin':
tryList = [
('home-0xdiag-datasets', 'airlines/year2013.csv', None, None, 'cE', 300),
(None, '/home/kevin/Downloads/t.csv', 15, 11, 'cE', 300),
]
else:
tryList = [
('home-0xdiag-datasets', 'airlines/year2013.csv', None, None, 'cE', 300),
]
# h2b.browseTheCloud()
trial = 0
for (bucket, csvPathname, iColCount, oColCount, hex_key, timeoutSecs) in tryList:
xList = []
eList = []
fList = []
# PARSE*******************************************************
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=200, doSummary=False)
csvPathnameFull = h2i.find_folder_and_filename(bucket, csvPathname, returnFullPath=True)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(key=parseResult['destination_key'])
h2o_cmd.infoFromInspect(inspect, csvPathname)
numRows = inspect['numRows']
numCols = inspect['numCols']
if not oColCount:
iColCount = 0
if not oColCount:
oColCount = numCols
colCount = iColCount + oColCount
for i in range (0,numCols):
print "Column", i, "summary"
h2o_cmd.runSummary(key=hex_key, max_qbins=1, cols=i);
# print h2o.dump_json(inspect)
levels = h2o.nodes[0].levels(source=hex_key)
print "levels result:", h2o.dump_json(levels)
(missingValuesDict, constantValuesDict, enumSizeDict, colTypeDict, colNameDict) = \
h2o_cmd.columnInfoFromInspect(parseResult['destination_key'], exceptionOnMissingValues=False)
# error if any col has constant values
if len(constantValuesDict) != 0:
# raise Exception("Probably got a col NA'ed and constant values as a result %s" % constantValuesDict)
print "Probably got a col NA'ed and constant values as a result %s" % constantValuesDict
# start after the last input col
levels = h2o.nodes[0].levels(source=hex_key);
l = levels['levels']
for column in range(iColCount, iColCount+oColCount):
if l[column]:
print "Skipping", column, "because it's enum (says levels)"
continue
# QUANTILE*******************************************************
quantile = 0.5 if DO_MEDIAN else .999
# first output col. always fed by an exec cut, so 0?
start = time.time()
# file has headers. use col index
q = h2o.nodes[0].quantiles(source_key=hex_key, column=column,
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=1)
qresult = q['result']
h2p.red_print("result:", q['result'], "quantile", quantile,
"interpolated:", q['interpolated'], "iterations", q['iterations'])
elapsed = time.time() - start
print "quantile end on ", hex_key, 'took', elapsed, 'seconds.'
quantileTime = elapsed
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
if 1==0:
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=True,
col=column, # what col to extract from the csv
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
# h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
use_genfromtxt=True,
)
trial += 1
execTime = 0
xList.append(column)
eList.append(execTime)
fList.append(quantileTime)
# remove all keys*******************************************************
# what about hex_key?
if 1==0:
start = time.time()
h2o.nodes[0].remove_all_keys()
elapsed = time.time() - start
print "remove all keys end on took", elapsed, 'seconds.'
#****************************************************************
# PLOTS. look for eplot.jpg and fplot.jpg in local dir?
if DO_PLOT:
xLabel = 'column (0 is first)'
eLabel = 'exec cut time'
fLabel = 'quantile time'
eListTitle = ""
fListTitle = ""
h2o_gbm.plotLists(xList, xLabel, eListTitle, eList, eLabel, fListTitle, fList, fLabel, server=True)
def test_exec2_quant_cmp_uniform(self):
h2o.beta_features = True
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
(500000, 1, 'x.hex', 1, 20000, ('C1', 1.10, 5000.0, 10000.0, 15000.0, 20000.00)),
(500000, 1, 'x.hex', -5000, 0, ('C1', -5001.00, -3750.0, -2445, -1200.0, 99)),
(100000, 1, 'x.hex', -100000, 100000, ('C1', -100001.0, -50000.0, 1613.0, 50000.0, 100000.0)),
(100000, 1, 'x.hex', -1, 1, ('C1', -1.05, -0.48, 0.0087, 0.50, 1.00)),
(100000, 1, 'A.hex', 1, 100, ('C1', 1.05, 26.00, 51.00, 76.00, 100.0)),
(100000, 1, 'A.hex', -99, 99, ('C1', -99, -50.0, 0, 50.00, 99)),
(100000, 1, 'B.hex', 1, 10000, ('C1', 1.05, 2501.00, 5001.00, 7501.00, 10000.00)),
(100000, 1, 'B.hex', -100, 100, ('C1', -100.10, -50.0, 0.85, 51.7, 100,00)),
(100000, 1, 'C.hex', 1, 100000, ('C1', 1.05, 25002.00, 50002.00, 75002.00, 100000.00)),
(100000, 1, 'C.hex', -101, 101, ('C1', -100.10, -50.45, -1.18, 49.28, 100.00)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, expectedMin, expectedMax, expected) in tryList:
# max error = half the bin size?
maxDelta = ((expectedMax - expectedMin)/20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, expectedMin, expectedMax, SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=30, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
# only one column
column = summaryResult['summaries'][0]
colname = column['colname']
self.assertEqual(colname, expected[0])
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
# FIX! we should compare mean and sd to expected?
mean = stats['mean']
sd = stats['sd']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats['zeros']
mins = stats['mins']
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg='min is not approx. expected')
maxs = stats['maxs']
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg='max is not approx. expected')
pct = stats['pct']
# the thresholds h2o used, should match what we expected
expectedPct = [0.001, 0.01, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.99, 0.999]
pctile = stats['pctile']
h2o_util.assertApproxEqual(pctile[3], expected[2], tol=maxDelta, msg='25th percentile is not approx. expected')
h2o_util.assertApproxEqual(pctile[5], expected[3], tol=maxDelta, msg='50th percentile (median) is not approx. expected')
h2o_util.assertApproxEqual(pctile[7], expected[4], tol=maxDelta, msg='75th percentile is not approx. expected')
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
print "pct:", pct
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows/len(hcnt)
# apparently we're not able to estimate for these datasets
# self.assertAlmostEqual(b, rowCount/len(hcnt), delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", compareActual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2p.blue_print("\nTrying exec quantile")
# thresholds = "c(0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99)"
# do the equivalent exec quantile?
# execExpr = "quantile(%s[,1],%s);" % (hex_key, thresholds)
print "Comparing (two places) each of the summary2 threshold quantile results, to single exec quantile"
for i, threshold in enumerate(thresholds):
# FIX! do two of the same?..use same one for the 2nd
if i!=0:
# execExpr = "r2=c(1); r2=quantile(%s[,4],c(0,.05,0.3,0.55,0.7,0.95,0.99))" % hex_key
execExpr = "r2=c(1); r2=quantile(%s[,1], c(%s,%s));" % (hex_key, threshold, threshold)
(resultExec, result) = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
h2p.green_print("\nresultExec: %s" % h2o.dump_json(resultExec))
h2p.blue_print("\nthreshold: %.2f Exec quantile: %s Summary2: %s" % (threshold, result, pt[i]))
if not result:
raise Exception("exec result: %s for quantile: %s is bad" % (result, threshold))
h2o_util.assertApproxEqual(result, pctile[i], tol=maxDelta,
msg='exec percentile: %s too different from expected: %s' % (result, pctile[i]))
# for now, do one with all, but no checking
else:
# This seemed to "work" but how do I get the key name for the list of values returned
# the browser result field seemed right, but nulls in the key
if 1==0:
execExpr = "r2=c(1); r2=quantile(%s[,1], c(%s));" % (hex_key, ",".join(map(str,thresholds)))
else:
# does this way work (column getting)j
execExpr = "r2=c(1); r2=quantile(%s$C1, c(%s));" % (hex_key, ",".join(map(str,thresholds)))
(resultExec, result) = h2e.exec_expr(execExpr=execExpr, timeoutSecs=30)
inspect = h2o_cmd.runInspect(key='r2')
numCols = inspect['numCols']
numRows = inspect['numRows']
self.assertEqual(numCols,1)
self.assertEqual(numRows,len(thresholds))
# FIX! should run thru the values in the col? how to get
# compare the last one
if colname!='':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=0, # what col to extract from the csv
datatype='float',
quantile=thresholds[-1],
# h2oSummary2=pctile[-1],
# h2oQuantilesApprox=result, # from exec
h2oExecQuantiles=result,
)
h2o.nodes[0].remove_all_keys()
def test_summary2_unifiles2(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
# new with 1000 bins. copy expected from R
tryList = [
# colname, (min, 25th, 50th, 75th, max)
('breadth.csv', 'b.hex', False, [ ('C1', None, None, None, None, None)], 'smalldata', 'quantiles'),
# ('wonkysummary.csv', 'b.hex', False, [ ('X1', 7, 22, 876713, 100008, 1000046)], 'smalldata', None),
('wonkysummary.csv', 'b.hex', True, [ ('X1', 7.00, None, None, None, 1000046.0)], 'smalldata', None),
('covtype.data', 'c.hex', False, [ ('C1', None, None, None, None, None)], 'home-0xdiag-datasets', 'standard'),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (csvFilename, hex_key, skipHeader, expectedCols, bucket, pathPrefix) in tryList:
if pathPrefix:
csvPathname = pathPrefix + "/" + csvFilename
else:
csvPathname = csvFilename
csvPathnameFull = h2i.find_folder_and_filename(bucket, csvPathname, returnFullPath=True)
if skipHeader:
header = 1
else:
header = 0
parseResult = h2i.import_parse(bucket=bucket, path=csvPathname,
schema='put', header=header, hex_key=hex_key, timeoutSecs=10, doSummary=False)
print "Parse result['destination_key']:", parseResult['destination_key']
# We should be able to see the parse result?
inspect = h2o_cmd.runInspect(None, parseResult['destination_key'])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
# okay to get more cols than we want
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
summaries = summaryResult['summaries']
scipyCol = 0
for expected, column in zip(expectedCols, summaries):
colname = column['colname']
if expected[0]:
self.assertEqual(colname, expected[0])
quantile = 0.5 if DO_MEDIAN else OTHER_Q
q = h2o.nodes[0].quantiles(source_key=hex_key, column=scipyCol,
quantile=quantile, max_qbins=MAX_QBINS, multiple_pass=2)
qresult = q['result']
qresult_single = q['result_single']
qresult_iterations = q['iterations']
qresult_interpolated = q['interpolated']
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", qresult_iterations)
h2p.blue_print("h2o quantiles interpolated:", qresult_interpolated)
print h2o.dump_json(q)
self.assertLess(qresult_iterations, 16,
msg="h2o does max of 16 iterations. likely no result_single if we hit max. is bins=1?")
# ('', '1.00', '25002.00', '50002.00', '75002.00', '100000.00'),
coltype = column['type']
nacnt = column['nacnt']
stats = column['stats']
stattype= stats['type']
print stattype
# FIX! we should compare mean and sd to expected?
# enums don't have mean or sd?
if stattype!='Enum':
mean = stats['mean']
sd = stats['sd']
zeros = stats['zeros']
mins = stats['mins']
maxs = stats['maxs']
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
pct = stats['pct']
print "pct:", pct
print ""
# the thresholds h2o used, should match what we expected
pctile = stats['pctile']
# hack..assume just one None is enough to ignore for cars.csv
if expected[1]:
h2o_util.assertApproxEqual(mins[0], expected[1], rel=0.02, msg='min is not approx. expected')
if expected[2]:
h2o_util.assertApproxEqual(pctile[3], expected[2], rel=0.02, msg='25th percentile is not approx. expected')
if expected[3]:
h2o_util.assertApproxEqual(pctile[5], expected[3], rel=0.02, msg='50th percentile (median) is not approx. expected')
if expected[4]:
h2o_util.assertApproxEqual(pctile[7], expected[4], rel=0.02, msg='75th percentile is not approx. expected')
if expected[5]:
h2o_util.assertApproxEqual(maxs[0], expected[5], rel=0.02, msg='max is not approx. expected')
# figure out the expected max error
# use this for comparing to sklearn/sort
if expected[1] and expected[5]:
expectedRange = expected[5] - expected[1]
# because of floor and ceil effects due we potentially lose 2 bins (worst case)
# the extra bin for the max value, is an extra bin..ignore
expectedBin = expectedRange/(MAX_QBINS-2)
maxErr = 0.5 * expectedBin # should we have some fuzz for fp?
# hack?
maxErr = maxErr * 2
print "maxErr:", maxErr
else:
print "Test won't calculate max expected error"
maxErr = 0
hstart = column['hstart']
hstep = column['hstep']
hbrk = column['hbrk']
hcnt = column['hcnt']
for b in hcnt:
# should we be able to check for a uniform distribution in the files?
e = .1 * numRows
# self.assertAlmostEqual(b, .1 * rowCount, delta=.01*rowCount,
# msg="Bins not right. b: %s e: %s" % (b, e))
if stattype!='Enum':
pt = h2o_util.twoDecimals(pctile)
print "colname:", colname, "pctile (2 places):", pt
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
actual = mn[0], pt[3], pt[5], pt[7], mx[0]
print "min/25/50/75/max colname:", colname, "(2 places):", actual
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
## ignore for blank colnames, issues with quoted numbers
# covtype is too big to do in scipy
if colname!='' and expected[scipyCol] and csvFilename!= 'covtype.data':
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=skipHeader, # important!!
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else OTHER_Q,
h2oSummary2=pctile[5 if DO_MEDIAN else OTHER_Q_SUMM_INDEX],
h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)
scipyCol += 1
trial += 1
def test_summary2_small(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
tryList = [
# colname, (min, 25th, 50th, 75th, max)
# if rowCount is None, we'll just use the data values
# None in expected values means no compare
(None, 1, "x.hex", [-1, 0, 1], ("C1", None, None, 0, None, None)),
(None, 2, "x.hex", [-1, 0, 1], ("C1", None, None, 0, None, None)),
(None, 10, "x.hex", [-1, 0, 1], ("C1", None, None, 0, None, None)),
(None, 100, "x.hex", [-1, 0, 1], ("C1", None, None, 0, None, None)),
(None, 1000, "x.hex", [-1, 0, 1], ("C1", None, None, 0, None, None)),
# (None, 10000, 'x.hex', [-1,0,1], ('C1', None, None, 0, None, None)),
# (COLS, 1, 'x.hex', [1,0,-1], ('C1', None, None, None, None, None)),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, values, expected) in tryList:
# max error = half the bin size?
expectedMax = max(values)
expectedMin = min(values)
maxDelta = ((expectedMax - expectedMin) / 20.0) / 2.0
# add 5% for fp errors?
maxDelta = 1.05 * maxDelta
# hmm...say we should be 100% accurate for these tests?
maxDelta = 0
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
if not rowCount:
rowFile = len(values)
else:
rowFile = rowCount
csvFilename = "syn_" + "binary" + "_" + str(rowFile) + "x" + str(colCount) + ".csv"
csvPathname = SYNDATASETS_DIR + "/" + csvFilename
print "Creating random", csvPathname
write_syn_dataset(csvPathname, rowCount, colCount, values, SEEDPERFILE)
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(
path=csvPathname, schema="put", hex_key=hex_key, timeoutSecs=30, doSummary=False
)
print "Parse result['destination_key']:", parseResult["destination_key"]
inspect = h2o_cmd.runInspect(None, parseResult["destination_key"])
print "\n" + csvFilename
numRows = inspect["numRows"]
numCols = inspect["numCols"]
summaryResult = h2o_cmd.runSummary(key=hex_key, max_qbins=MAX_QBINS, timeoutSecs=45)
h2o.verboseprint("summaryResult:", h2o.dump_json(summaryResult))
quantile = 0.5 if DO_MEDIAN else 0.999
q = h2o.nodes[0].quantiles(
source_key=hex_key,
column=0,
interpolation_type=7,
quantile=quantile,
max_qbins=MAX_QBINS,
multiple_pass=2,
)
qresult = q["result"]
qresult_single = q["result_single"]
qresult_iterations = q["iterations"]
qresult_interpolated = q["interpolated"]
h2p.blue_print("h2o quantiles result:", qresult)
h2p.blue_print("h2o quantiles result_single:", qresult_single)
h2p.blue_print("h2o quantiles iterations:", qresult_iterations)
h2p.blue_print("h2o quantiles interpolated:", qresult_interpolated)
print h2o.dump_json(q)
self.assertLess(
qresult_iterations,
16,
msg="h2o does max of 16 iterations. likely no result_single if we hit max. is bins=1?",
)
# only one column
column = summaryResult["summaries"][0]
colname = column["colname"]
coltype = column["type"]
nacnt = column["nacnt"]
stats = column["stats"]
stattype = stats["type"]
# FIX! we should compare mean and sd to expected?
mean = stats["mean"]
sd = stats["sd"]
print "colname:", colname, "mean (2 places):", h2o_util.twoDecimals(mean)
print "colname:", colname, "std dev. (2 places):", h2o_util.twoDecimals(sd)
zeros = stats["zeros"]
mins = stats["mins"]
maxs = stats["maxs"]
pct = stats["pct"]
# the thresholds h2o used, should match what we expected
expectedPct = [0.01, 0.05, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.95, 0.99]
pctile = stats["pctile"]
print "pctile:", pctile
if expected[0]:
self.assertEqual(colname, expected[0])
if expected[1]:
h2o_util.assertApproxEqual(mins[0], expected[1], tol=maxDelta, msg="min is not approx. expected")
if expected[2]:
h2o_util.assertApproxEqual(
pctile[3], expected[2], tol=maxDelta, msg="25th percentile is not approx. expected"
)
if expected[3]:
h2o_util.assertApproxEqual(
pctile[5], expected[3], tol=maxDelta, msg="50th percentile (median) is not approx. expected"
)
if expected[4]:
h2o_util.assertApproxEqual(
pctile[7], expected[4], tol=maxDelta, msg="75th percentile is not approx. expected"
)
if expected[5]:
h2o_util.assertApproxEqual(maxs[0], expected[5], tol=maxDelta, msg="max is not approx. expected")
hstart = column["hstart"]
hstep = column["hstep"]
hbrk = column["hbrk"]
hcnt = column["hcnt"]
print "pct:", pct
print ""
print "hcnt:", hcnt
print "len(hcnt)", len(hcnt)
# don't check the last bin
for b in hcnt[1:-1]:
# should we be able to check for a uniform distribution in the files?
e = numRows / len(hcnt) # expect 21 thresholds, so 20 bins. each 5% of rows (uniform distribution)
# don't check the edge bins
self.assertAlmostEqual(
b, numRows / len(hcnt), delta=1 + 0.01 * numRows, msg="Bins not right. b: %s e: %s" % (b, e)
)
pt = h2o_util.twoDecimals(pctile)
mx = h2o_util.twoDecimals(maxs)
mn = h2o_util.twoDecimals(mins)
print "colname:", colname, "pctile (2 places):", pt
print "colname:", colname, "maxs: (2 places):", mx
print "colname:", colname, "mins: (2 places):", mn
# FIX! we should do an exec and compare using the exec quantile too
compareActual = mn[0], pt[3], pt[5], pt[7], mx[0]
h2p.green_print("min/25/50/75/max colname:", colname, "(2 places):", compareActual)
print "maxs colname:", colname, "(2 places):", mx
print "mins colname:", colname, "(2 places):", mn
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
# don't check if colname is empty..means it's a string and scipy doesn't parse right?
if colname != "":
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
col=scipyCol, # what col to extract from the csv
datatype="float",
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=pctile[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
h2oQuantilesExact=qresult,
)
def test_summary2_exp(self):
SYNDATASETS_DIR = h2o.make_syn_dir()
LAMBD = random.uniform(0.005, 0.5)
tryList = [
# co.label, (min, 25th, 50th, 75th, max)
# parse setup error
# (1, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
(5, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
# (10, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
# (100, 1, 'x.hex', 1, 20000, ['C1', None, None, None, None, None]),
# (1000, 1, 'x.hex', -5000, 0, ['C1', None, None, None, None, None]),
# (10000, 1, 'x.hex', -100000, 100000, ['C1', None, None, None, None, None]),
# (100000, 1, 'x.hex', -1, 1, ['C1', None, None, None, None, None]),
# (1000000, 1, 'A.hex', 1, 100, ['C1', None, None, None, None, None]),
]
timeoutSecs = 10
trial = 1
n = h2o.nodes[0]
lenNodes = len(h2o.nodes)
x = 0
timeoutSecs = 60
for (rowCount, colCount, hex_key, rangeMin, rangeMax, expected) in tryList:
SEEDPERFILE = random.randint(0, sys.maxint)
x += 1
csvFilename = 'syn_' + "binary" + "_" + str(rowCount) + 'x' + str(colCount) + '.csv'
csvPathname = SYNDATASETS_DIR + '/' + csvFilename
print "Creating random", csvPathname, "lambd:", LAMBD
(expectedMin, expectedMax) = write_syn_dataset(csvPathname, rowCount, colCount, lambd=LAMBD, SEED=SEEDPERFILE)
print "expectedMin:", expectedMin, "expectedMax:", expectedMax
maxErr = ((expectedMax - expectedMin)/20.0) / 2.0
# add 5% for fp errors?
maxErr = 1.05 * maxErr
expected[1] = expectedMin
expected[5] = expectedMax
csvPathnameFull = h2i.find_folder_and_filename(None, csvPathname, returnFullPath=True)
parseResult = h2i.import_parse(path=csvPathname, schema='put', hex_key=hex_key, timeoutSecs=30, doSummary=False)
pA = h2o_cmd.ParseObj(parseResult, expectedNumRows=rowCount, expectedNumCols=colCount)
print pA.numRows, pA.numCols, pA.parse_key
iA = h2o_cmd.InspectObj(pA.parse_key,
expectedNumRows=rowCount, expectedNumCols=colCount, expectedMissinglist=[])
print iA.missingList, iA.labelList, iA.numRows, iA.numCols
# column 0 not used here
assert len(expected) == 6
co = h2o_cmd.runSummary(key=hex_key, column=0, expected=expected[1:], maxDelta=maxErr)
print co.label, co.type, co.missing, co.domain, sum(co.bins)
# default_pctiles
# isText
# rows
# off
# key
# checksum
# touch all that should be there
coList = [co.base, len(co.bins), len(co.data), co.domain, co.label, co.maxs, co.mean, co.mins, co.missing,
co.ninfs, co.pctiles, co.pinfs, co.precision, co.sigma, co.str_data, co.stride, co.type, co.zeros]
for k,v in co:
print k, v
trial += 1
h2o.nodes[0].remove_all_keys()
scipyCol = 0
print "maxErr", maxErr
if co.label!='' and expected[scipyCol]:
# don't do for enums
# also get the median with a sort (h2o_summ.percentileOnSortedlist()
h2o_summ.quantile_comparisons(
csvPathnameFull,
skipHeader=False,
col=scipyCol,
datatype='float',
quantile=0.5 if DO_MEDIAN else 0.999,
h2oSummary2=co.pctiles[5 if DO_MEDIAN else 10],
# h2oQuantilesApprox=qresult_single,
# h2oQuantilesExact=qresult,
h2oSummary2MaxErr=maxErr,
)